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Full-dimensional neural network potential energy surface and dynamics of the CH 2 OO + H 2 O reaction.

Hao WuYan-Lin FuWenrui DongBina FuDonghui Zhang
Published in: RSC advances (2023)
An accurate global full-dimensional machine learning-based potential energy surface (PES) of the simplest Criegee intermediate (CH 2 OO) reaction with water monomer was developed based on the high level of extensive CCSD(T)-F12a/aug-cc-pVTZ calculations. This analytical global PES not only covers the regions of reactants to hydroxymethyl hydroperoxide (HMHP) intermediates, but also different end product channels, which facilities both the reliable and efficient kinetics and dynamics calculations. The rate coefficients calculated by the transition state theory with the interface to the full-dimensional PES agree well with the experimental results, indicating the accuracy of the current PES. Extensive quasi-classical trajectory (QCT) calculations were performed both from the bimolecular reaction CH 2 OO + H 2 O and from HMHP intermediate on the new PES. The product branching ratios of hydroxymethoxy radical (HOCH 2 O, HMO) + OH radical, formaldehyde (CH 2 O) + H 2 O 2 and formic acid (HCOOH) + H 2 O were calculated. The reaction yields dominantly HMO + OH, because of the barrierless pathway from HMHP to this channel. The computed dynamical results for this product channel show the total available energy was deposited into the internal rovibrational excitation of HMO, and the energy release in OH and translational energy is limited. The large amount of OH radical found in the current study implies that the CH 2 OO + H 2 O reaction can provide crucially OH yield in Earth's atmosphere.
Keyphrases
  • room temperature
  • density functional theory
  • machine learning
  • neural network
  • molecular dynamics simulations
  • high resolution
  • electron transfer
  • mass spectrometry
  • computed tomography
  • deep learning
  • monte carlo